Devices and methods employing hermetic transforms for encoding and decoding digital information in spread-spectrum communications systems

ABSTRACT

In a direct sequence spread-spectrum (DSSS) systems, such as CDMA, information is encoded in symbols using phase shift keying or quadrature amplitude modulation. Further, a transmitter applied a selected time shifted lag to each symbol to convey additional information. A receiver detects both the symbol data and the lag value. The receiver can use a hermetic matched filter matrix to identify the lag.

CROSS-REFERENCE

This application claims priority under 35 U.S.C. §119(e) to provisionalapplication Ser. No. 61/895,577, filed Oct. 25, 2013, which isincorporated entirely by reference.

BACKGROUND

Communications systems employ various means of allowing multiple usersto send data streams within an allocated portion of the communicationsfrequency spectrum in a shared manner. These means include time-divisionmultiple access (TDMA); frequency-division multiple access (FDMA);orthogonal frequency division multiplexing (OFDM), with carrier sensemultiple access (CSMA); and code division multiple access (CDMA).Performance of these systems can be limited by the canonical algorithmicapproaches for frequency spectral analysis and temporal correlationanalysis/matched filtering.

Major advances in communications, especially related to cellulartelephone systems, have been achieved as a result of spread-spectrum,Code Division Multiple Access (CDMA).

FIG. 1 shows a known generalized CDMA system for explanatory purposes. Abinary information sequence of “1s” and “0s” (data bits) is converted tophase-shift forms, e.g., a “1” corresponds to no phase shift, and a “0”corresponds to a 180-degree phase shift. This is referred to as binaryphase shift keying (BPSK), although other methods that use more phaseshifts and multiple amplitudes, such as QPSK or QAM-64, could be used.These phase shifts can be applied with a particular keying rate to anintermediate frequency subcarrier signal. Next, orthogonal orquasi-orthogonal binary codes (e.g., Walsh Codes and/or Pseudo-Noise PNCodes) are applied in a similar fashion to channelize signals(especially via the Walsh code) and to spread the signal spectrallyacross a much wider bandwidth than the signal itself requires (e.g.,1.25 MHz for 3GPP2 specifications, and 5 MHz for 3GPP specifications).The signal is modulated onto a radio-frequency carrier signal,amplified, and fed to a transmitting antenna system. Each code isassigned to a different communications channel. With properorthogonality of the codes, each channel can occupy the same frequencyspace 100% of the time and still be decoded and despread by receivers.At the receiving end, the RF carrier is demodulated and code correlatedwith a correlator or a matched filter to despread the signal, leaving aninformation sequence of 1's and −1's that are re-interpreted as binaryones and zeros. While shown with two steps of frequency conversion,often one step (direct conversion) is used from baseband to RF and viceversa.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a known CDMA transmitter.

FIGS. 2-3 are block diagrams of a CDMA transmitter and receiveraccording to some embodiments.

FIGS. 4-5 are graphs showing received signals according to someembodiments.

FIGS. 6-7 are block diagrams showing creation and use of a hermeticfilter.

FIGS. 8 is a graph showing a timing bit.

FIGS. 9-11 are graphs of examples of received signals.

FIG. 12 is a block diagram of a RAKE receiver.

FIGS. 13-14 are graphs of received signals.

Description

Direct sequence spread-spectrum (DSSS) systems, such as CDMA, can beused to encode and decode additional information and to increase thedata-carrying capacity of such systems. In one example, it is shown thatchanges to the signaling scheme for CDMA allows significantly increaseddata rate performance through enhanced receive processing that can beperformed with Hermetic Transforms. These transforms are described inmore detail in U.S. Pat. No. 8,064,408, which is incorporated herein byreference.

Referring to FIG. 2, the present disclosure, referred to as HermeticCDMA or “H-CDMA” uses a similar general approach to the known approachfor transmitting with CDMA, but with modifications. A block diagram ofthe H-CDMA signal generation is shown in a manner that receives a signalfrom the process shown and described in conjunction with FIG. 1.

The H-CDMA utilizes the idea that code correlation using conventionalconvolution or FFT-based technology has a limit in time resolution whichis on the order of the reciprocal of the spread signal bandwidth. Forexample, a 1 MHz wide spread signal will have a correlation peak on theorder of one microsecond. Another idea that is exploited here is thatfor oversampled signals, use of Hermetic Transform Correlation orHermetic Matched Filtering can allow higher temporal resolution, andthat this resolution can be used for information-carrying.

Referring to FIG. 2, a digital form of a given coded signal,super-imposed on its sub-carrier, can be circularly shifted to transmitadditional information via a circular shift sequence lag function toprovide a sum of shifted sequences. A Hermetic Matched Filter (HMF)matrix (discussed in more detail below) maybe constructed to resolvecircularly shifted versions of the given coded signal at the receiverend, and the shift information (lag) identified via a second stage ofprocessing via the HMF processing stage. In other words, in addition tothe “regular” information that would be transmitted with a CDMAtransmitter, the transmitted signal can also have a lag that can beintroduced and detected. This lag information can carry additionalinformation as data. As mentioned with respect to FIG. 1, modulationother than BPSK can be used, and the signal could be directlyupconverted.

Referring to FIG. 3, the received data processing approach is shown inmore detail. In this embodiment, the received signal is quadraturedemodulated and analog-to-digital converted. A correlation process isperformed on the digital signal with a replica (known and expected code)correlated against the received complex coded signal. The location of acode correlation peak determines an interval of the complex correlationfunction that can be utilized in the subsequent stages of processing.The complex correlation output is provided to a function that determinesthe real part at peak index. The SIGN function then receives the realpart of the complex code correlation output at a lag corresponding tothe peak correlation of the CDMA information bit stream (+/−1's). Thisis similar to a typical CDMA decoding process.

In additional, the receiver identifies a peak index and captures acorrelation segment around the peak. The output is processed with aFourier Transform or Hermetic Transform and then multiplied by the HMFmatrix. The peak location of this result indicates the circular shiftlag of the received signal. Multiple circularly shifted versions of thebasic code can potentially be added together to send multiple lags asanother layer of information.

Referring to FIG. 4, a MATLAB™ experiment was done with a 5 MHz wideCDMA type signal (the bandwidth typically used in VMTS or W-CDMA), wherea sampling rate is chosen to provide a correlation peak that isapproximately 200 lags or samples wide. A set of circularly shiftedversions of the CDMA codes were created with lags ranging from −50 to 50samples relative to a center peak. The Hermetic Matched Filter wascreated so as to optimally separate these shifted codes and resolve themto recover data.

Referring to FIG. 5, each frame has a lag value. The results show the“sent lags” versus the “detected lags” for several ensembles of binaryinformation. The figure indicates that the data was successfulrecovered. The result shows that 101 independent lag positions (from −50to 50) corresponding to log₂(101)=6.7 bits of additional information,can be sent, providing nearly an eight-fold improvement overconventional CDMA using BPSK. With additional oversampling and use ofmultiple sums of shifted replications, higher rates may be achieved. Theconcepts here also apply to the case of circular frequency shifting anduse of ambiguity function time-frequency processing instead ofcorrelation. In addition, amplitude and/or quadrature amplitudeinformation might be impressed on each of the shifted codes so that evenmore information may be transmitted.

For a simplified example, suppose that each symbol has 4 bits of data(as with 16-QAM), and suppose there are 4 possible lags (rather than the100 or more that is possible as noted above). This means that eachsymbol of 4 bits, combined with 2 bits that are detected from one of the4 lags, can be used to encode 6 bits per symbol rather than 4 bits persymbol.

Hermetic Matched Filter

In H-CDMA, the performance at the receiver can be improved by usingHermetic-Transform Matched Filter (HTMF) correlation processing. Asnoted above, the concepts relating to Hermetic processing are describedin the incorporated patent. Essentially, the conventional replicacorrelation used in code processing or replica matched-filter as used inconventional spread-spectrum systems is replaced with HTMF correlation.Gains accrue from both enhanced time-resolution in channel estimation,and in the potential update rate in channel estimation.

Referring to FIG. 6, the HTMF is created from the signal replica (theknown code). The signal replica is assumed to be an array ofcomplex-valued (c-number) time samples generated from a real-dataderived from in-phase and quadrature sampling or via a HilbertTransform. The signal is assumed to be band-limited. The replica isquadrature sampled at an oversampling rate f_(s) that is significantlygreater than the Nyquist rate, f_(N). A plurality of vectors areproduced by circularly shifting the signal vector by a particular numberN of time samples (the “lags”) and by applying a spectral transformation(FFT/DFT or Hermetic Transform). One vector is produced for each of Nlag values, and these N samples are buffered. Each of these vectors isarranged as a column in a matrix referred to as a Signature Matrix (E).This matrix is then utilized to create a Hermetic Transform MatchedFilter Matrix (H) which is constructed as the minimum quadratic normsolution to the matrix equation HΣ=I, where I is the identity matrix.The HMF matrix H is generated by a processing system and stored inmemory.

Referring to FIG. 7, in applying HTMF correlation to unknown signals atthe receiver, it is assumed (as in conventional matched filtering) thatthe signal consists of the original replica changed only by a time-shiftlag and amplitude scaling (channel gain). The HTMF is applied to theunknown signal (Signal In) after application of the same spectraltransform (FFT/DFT or DHT) as was used in the creation of the SignatureMatrix. A similar process of oversampling and buffering is performed. Atransform is applied to the buffered data, and the result is arranged asa column vector F. This column vector is multiplied by the stored HMFmatrix H.

If the input signal were precisely the same as a replica of the originalsignal with time lag L, application of the transform matrix H to vectorF will produce a “′1” in the Lth row of the column vector output V, andessentially zeroes everywhere else in the vector. The process is linear,so that a linear superposition of scaled time-lagged versions of thesignal as an input will produce a corresponding linear superposition ofoutput vectors. In the case of H-CDMA, the signal replica represents thespreading code or a version of the code that has been conventionallymatched filtered. Any type of information sequence applied to thisreplica at the information signaling rate (e.g. BPSK/QPSK, QAM, etc.)will be preserved because of linearity of the HTMF process.

Lag-Division Multiplexing (LDM)

Still referring to FIG. 7, the last processing step shown (Threshold &Find Peak(s) Location(s)) provides an example of how the HTMF output canbe used to detect the time lag in a signal as information. This processuses thresholding on the matched-filter output to determining peaks inthe HTMF output.

Referring to FIG. 8, as an example of one embodiment, assumeconventional replica correlation with the spreading code produces anoutput as shown. A conventional matched filter produces a broad peak intime, shown here as the INFO bit, which is on the order of approximately1/W seconds in time, where W is the spread bandwidth of signal (e.g., aCDMA signal).

Referring to FIG. 9 (which is similar to FIG. 4), a range of lags isselected to lie within the main lobe of the correlation peak as shown.For purposes of illustration, lags from −50 samples to 50 samples havebeen selected, and therefore there are 101 possible lag positions. Oneor more of these signals can be transmitted in each frame. Assume asingle lagged version of the signal is transmitted out of 100 possiblepositions for a total of log₂(100) bits of additional information beingtransmitted by shifting the replica. A conventional CDMA correlatormight typically be unable to identify such small time shifts due to theapproximately 1/W time resolution limit on replica correlation, but theHTMF correlation can detect the lag with more precision.

Referring again to FIG. 8, in one embodiment, the zero-lag version ofthe signal is reserved as a synchronization/TIMING bit and for use inmulti-path channel response estimation. Therefore, each frame representsthe sum of a zero-lag version of the code plus a non-zero lag version(INFO bit) of the code. The figure indicates the 101 positions from lags−50 to 50 (or shown as 0 to 100) samples on the x-axis and the outputrepresents the amplitude of the HTMF output. The zero-lag codecorrelation peak from the HTMF shown as TIMING bit lies in the middle ofthe frame, while the non-zero lag peak, INFO bit, is also clearly seen.Different amplitudes have been used for presentation purposes. The peakwith smaller amplitude is the “synch” signal (zero-lag) while thelocation of the larger peak provides additional bits of informationthrough its position relative to the zero-lag location, rather likepulse-position modulation. In FIG. 7, the distance between the INFO bitand the TIMING bit is approximately 20 lags/samples, but it isdetermined precisely to recover information. This is referred to here asLag-Division Multiplexing (LDM).

The system can use 2, 4, 8, 16, 32, 64, etc., lags to transmit 1, 2, 3,4, 5, 6, etc., bits of data.

Hermetic RAKE Receiver (HRR) and Hermetic Equalizer Processing

It is often the case, for example in a cellular telephone application(but not limited to such an application), that the signal received atthe handset is related in a complex manner to the transmitted signal. Inparticular, the signal may have propagated from the base station(transmitting point) to the handset (receiving point) along severalpaths, each of which may have introduced additional amplitude, phase,and time delay modification to the signal. The use of signal diversityprocessing is common in order to gain signal to noise ratio and to avoidbit errors. In the case of the H-CDMA type processing, the success ofutilizing lagged versions of the signal code could be disrupted withoutcompensation for channel corruption. The Hermetic Transform MatchedFilter assists with this problem.

A traditional RAKE architecture used in receivers can be modifiedapplying HTMF correlation as a replacement for conventional correlationin channel estimation, in order to derive parameters for either a RAKEreceiver or equalizer (or both). This process creates two categories ofimprovement, namely (1) improved multipath time delay estimation, andtherefore better channel estimation; and (2) potential use of smallerdata blocks for estimation, and therefore better ability to adapt tochanges of channel conditions in mobile or other non-stationarystatistics condition.

Channel Estimation and RAKE Processing using Hermetic Transform MatchedFilter

The present disclosure accomplishes diversity processing using one ormore antennas to accomplish RAKE processing. Channel estimation isaccomplished by applying the HTMF to a known “probe signal” which can beconstructed and used especially for this purpose, or alternatively, theseries of “synch” (zero-lag) codes embedded in every frame of data canbe utilized to determine the channel model parameters used in the RAKEreceiver.

A model and diagram for conventional RAKE receiver processing used asone embodiment is taught by Proakis, “Digital Communications” (McGrawHill, 1983, p. 471). The model is that of a tapped delay line of FiniteImpulse Response (FIR) form with complex weights and delayscorresponding to the unknown channel. An optimum form of RAKE correlatoris derived for this model in the reference, for the case where the tapweights are known, and the tap spacing is determined by the timeresolution of the matched filter correlator (tap spacing=approximately1/W). The architecture shown in the text book need only be modified,i.e., the tap delays are reduced to accommodate the much higher timeresolution of the HTMF, and channel estimation uses HTMF instead ofreplica correlation.

FIG. 10 shows HTMF output versus time with a real part of a randomcalibration signal sequence processed with the HTMF. The “spikes” in theHTMF output correspond to coded signals with various lags.

FIG. 11 shows the output of a channel-corrupted version of this sequenceprocessed with the identical HTMF. These signals are input to a channelestimation model constrained to be of the FIR form. The channel modelcan use a simplistic model, two paths with one path having unity gainand one delayed by 75 samples (marginally detectable using conventionalcorrelation processing) with a gain of 0.5 and a 180 degree phase shift.Thus all of the tap weights are zero except for the zero-lag and the75-sample lag terms.

The channel estimator used was the Steiglitz-McBride Iteration (part ofthe standard MATLAB™ Signal Processing Toolbox). The routine takes theinput sequence and output sequences and form these moving-average (FIR)and auto-regressive (AR) filter coefficients. The number of ARcoefficients was set to zero and the result is an FIR, tapped-delaymodel. The channel estimation is shown in FIG. 11, with amplitude andphase of the weights precisely correct. There are a variety ofalgorithms which can be used for this part of the process, which arefamiliar to one with ordinary skill in the art. Alternatively, thechannel response could be used to create an inverse filter, which thencould be used to equalize the signal and remove channel effects; thisapproach is less common but not always infeasible.

FIG. 12 shows a block diagram of a Hermetic version of a RAKE receiver.The received signal is split in two paths—one to a data buffer and thenlinear filter to provide an output signal with improved SNR. In theother path, the signal is provided to a HMF path for estimating pathdelays and amplitudes to adjust the linear filter.

FIGS. 13 and 14 are examples of real data of a pulsed signal (shown asreplica), which is being “replica correlated” or equivalently“matched-filtered” using the HMF versus a conventional matched filter,and shows a resolution enhancement.

Other embodiments are within the following claims. The inventionsdescribed here include the ability to add time lag information to asignal to enhance the amount of data carrier, and particularly to a DSSSsignal, and more particularly with a Hermetic matched filter. Thesystems and methods can be used for CDM and CDMA systems, including formobile terminals and base stations (including access points). Asindicated in the incorporated patent, the implementation can be madewith any form of suitable processor, including general or specificpurpose processors, including processing logic, and would typically bein a system that includes memory and other associated processing. In acommunications system, the implementation would typically reside in theMAC/PHY layers, and could be implemented with hardware or softwarelogic.

What is claimed is:
 1. A receiver method comprising: receiving a directsequence spread spectrum (DSSS) signal with a combination of datasymbols and a code; deriving data from the data symbols; determining oneof a plurality of different time-shifted lag values of the received coderelative to a known reference value; deriving encoded data based on thelag value; outputting data derived from both the data symbols and fromthe lag value, wherein determining the lag value includes generating aplurality of vectors by shifting an input signal vector by a number N oftime samples and applying a transform to produce a transformed signal;and wherein determining the lag value further includes providing thetransformed signal to a hermetic matched filter matrix to produce ahermetic matched filtered signal.
 2. The method of claim 1, whereinreceiving the direct sequence spread spectrum signal with a combinationof data symbols and a code includes oversampling the received signal. 3.The method of claim 1, wherein the receiving includes quadraturedemodulating and converting a received analog signal to a digitalsignal.
 4. The method of claim 1, wherein each symbol represents n bits,and wherein the lag value represents m bits, such that a lagged symbolrepresents n +m bits.
 5. The method of claim 1, wherein the DSSS signalis received by a mobile device.
 6. The method of claim 1, wherein theDSSS signal is received by a base station.
 7. The method of claim 1,wherein the receiving includes receiving symbols encoded with phaseshift keying (PSK) or quadrature amplitude modulation (QAM).
 8. Themethod of claim 1, wherein determining the lag value includesdetermining one of at least 4 values, and determining encoded dataincludes deriving at least two bits from the lag value.
 9. A receivercomprising an antenna for receiving RF signals: memory for storinginstructions; and a processor for executing instructions, the receiverconfigured for: receiving a direct sequence spread spectrum (DSSS)signal with a combination of data symbols and a code; deriving data fromthe data symbols; determining one of a plurality of differenttime-shifted lag values of the received code relative to a knownreference value; deriving encoded data based on the lag value;outputting data derived from both the data symbols and from the lagvalue, wherein determining the lag value includes generating a pluralityof vectors by shifting an input signal vector by a number N of timesamples and applying a transform to produce a transformed signal; andwherein determining the lag value further includes providing thetransformed signal to a hermetic matched filter matrix to produce ahermetic matched filtered signal.
 10. The receiver of claim 9, whereinthe receiver is part of a mobile device.
 11. The receiver of claim 9,wherein the receiver is part of a base station.
 12. The receiver ofclaim 9, wherein receiving the direct sequence spread spectrum signalwith a combination of data symbols and a code includes oversampling thereceived signal.
 13. A transmitter method comprising: encoding data assymbols; applying one of a plurality of possible sequence lag values toa spreading code to create a time-shifted spreading code relative to areference value, wherein the lag value represents coded data; combiningthe time-shifted spreading code and symbols to produce a spread spectrumsignal that combines the symbols with the code at the applied lag value,where a spacing between lag values is less than 1 divided by a bandwidthof the spread spectrum signal; and transmitting the combined signal toprovide a signal including data represented by encoded symbols and datarepresented by the lag value.
 14. The method of claim 13, wherein thedata symbols represent one of phase shift keyed data and quadratureamplitude modulated data.
 15. The method of claim 14, wherein the datasymbol represent a number of bits n, and the lag value encodes a numberof bits m, such that each lagged symbol represents n +m bits.
 16. Themethod of claim 15, wherein the transmitting is performed by a mobiledevice.
 17. The method of claim 13, wherein the transmitting isperformed by a base station.
 18. The method of claim 13, wherein, theapplying includes applying one of four lag values.
 19. The method ofclaim 18, wherein the lag value encodes at least two bits per symbol.